Arc furnace performance validation thru modeling, monitoring and statistical analysis

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Abstract
With recent advances in technology and energy conservation efforts, strides in design and powering AC electric arc furnace (EAF) steelmaking are being achieved. In the early days of furnace operation, the furnace operators relied on analog meters for power, energy, voltage, current and limited strip chart recordings to operate the furnace. Today, with digital data acquisition systems and abundant memory, a wealth of data beyond power quantities is available to the furnace operator in both real time and for post processing for each and every heat. Although the operator uses this data to track furnace performance and make minor adjustments, any significant changes are reserved for typically once a year when the furnace is “tuned”. Alternatively, this paper proposes assessing furnace performance on a more frequent basis utilizing this readily available data. A software tool is developed to compile the statistics and provide useful presentations of the furnace operation to enable decision making. For example, plotting the actual power curve data from a heat against that predicted by the furnace circuit model, or plotting the distribution of the phase current compared to regulator set point. This paper discusses the design model that powers the EAF along with statistical methods to optimize the operation of the EAF.